Interactive Web Application on “gapminder” Data

(I) Background

• Instructor: Peng Wang, AVP, Head of Data Science - Operation & Fraud Detection at MassMutual Financial Group (Fall 2016)
• The data set used in this project is gapminder from Bioconnector. The data set includes the data of Life Expectancy, Population, GDP per Capita of all the countries of each continent from 1952 to 2007 with an interval of 5 years.
• Download Data
• Multiple visualizations of the data set (Section II) and an interactive web application (Section III) were made in this project report.

(II) Data Analysis

gapminder_url = "https://bioconnector.github.io/workshops/data/gapminder.csv"

gapminder = read_csv(gapminder_url)

render_df = function(df) {
  row_size = df %>% dim() %>% magrittr::extract(1)
  output_table = df %>%
    kable(align = "c") %>%
    kable_styling(bootstrap_options = c("striped",
                                        "hover",
                                        "responsive",
                                        "condensed"),
                  fixed_thead = TRUE,
                  full_width = FALSE) %>%
    row_spec(0:row_size, extra_css = "vertical-align: middle;")
  return(output_table)
}

1. Number of countries per continent

• How many unique countries are represented per continent?

df_1 = gapminder %>%
  select(continent, country) %>%
  group_by(continent) %>%
  summarise(country = country %>% n_distinct()) %>%
  rename(Continent = continent,
         Country = country)

df_1 %>% render_df()

Continent	Country
Africa	52
Americas	25
Asia	33
Europe	30
Oceania	2

ggplot(data = df_1)+
  geom_col(mapping = aes(Continent, Country), width = 0.5) +
  ggtitle("Country Number of Each Continent") +
  theme(plot.title = element_text(size = 20, hjust = 0.5))

2. According to the data available, what was the average Life Expectancy across each continent from 1952 to 2007?

df_2=tapply(gapminder$lifeExp,list(gapminder$continent,gapminder$year),mean)
df_2=data.frame(t(df_2))
Year=seq(from=1952,to=2007,by=5)
df_2=cbind(Year,df_2)
df_2=gather(data=df_2,continent,Average.lifeExp,-Year)
p1=ggplot(data=df_2,aes(x=Year,y=Average.lifeExp,color=continent))+
  geom_point()+
  geom_line()+
  ggtitle("Average Life Expectancy")+
  xlab("Year")+
  ylab("Life Expectancy (Years)")+
  theme(plot.title=element_text(size=20,hjust=0.5))
p1

2.1 Life Expectancy for Every Countries in Americas

df_3=gapminder%>%filter(continent=="Americas")
df_3=tapply(df_3$lifeExp,list(df_3$country,df_3$year),mean)
df_3=t(data.frame(df_3))
row.names(df_3)=seq(1952,2007,5)
matplot(seq(1952,2007,5),df_3,type="l",lty=1,xlab="Years",ylab="Life Expectancy")

2.2 Countries that have the longest average Life Expectancy in the world

df_4=sort(tapply(gapminder$lifeExp,gapminder$country,mean),decreasing=TRUE)[1:5]
df_4=as.data.frame(df_4)
colnames(df_4)="Average Life Expectancy"
kable(df_4,align="c")

	Average Life Expectancy
Iceland	76.51142
Sweden	76.17700
Norway	75.84300
Netherlands	75.64850
Switzerland	75.56508

2.3 Countries that have the shortest average Life Expectancy in the world

df_5=sort(tapply(gapminder$lifeExp,gapminder$country,mean),decreasing=FALSE)[1:5]
df_5=as.data.frame(df_5)
colnames(df_5)="Average Life Expectancy"
kable(df_5,align="c")

	Average Life Expectancy
Sierra Leone	36.76917
Afghanistan	37.47883
Angola	37.88350
Guinea-Bissau	39.21025
Mozambique	40.37950

3. According to the data available, what was the average Population across each continent from 1952 to 2007?

df_6=tapply(gapminder$pop,list(gapminder$continent,gapminder$year),mean)
df_6=data.frame(t(df_6))
Year=seq(from=1952,to=2007,by=5)
df_6=cbind(Year,df_6)
df_6=gather(data=df_6,continent,pop,-Year)
p2=ggplot(data=df_6,aes(x=Year,y=pop,color=continent))+
  geom_point()+
  geom_line()+
  ggtitle("Average Population")+
  xlab("Year")+
  ylab("Population")+
  theme(plot.title=element_text(size=20,hjust=0.5))
p2

3.1 Population for Every Countries in Americas

df_7=gapminder%>%filter(continent=="Americas")
df_7=tapply(df_7$pop,list(df_7$country,df_7$year),mean)
df_7=t(data.frame(df_7))
row.names(df_7)=seq(1952,2007,5)
matplot(seq(1952,2007,5),df_7,type="l",lty=1,xlab="Years",ylab="Population")

4. According to the data available, what was the average GDP per Capita across each continent from 1952 to 2007?

df_8=tapply(gapminder$gdpPercap,list(gapminder$continent,gapminder$year),mean)
df_8=data.frame(t(df_8))
Year=seq(from=1952,to=2007,by=5)
df_8=cbind(Year,df_8)
df_8=gather(data=df_8,continent,gdpPercap,-Year)
p3=ggplot(data=df_8,aes(x=Year,y=gdpPercap,color=continent))+
  geom_point()+
  geom_line()+
  ggtitle("Average GDP per Capita")+
  xlab("Year")+
  ylab("GDP per Capita")+
  theme(plot.title=element_text(size=20,hjust=0.5))
p3

4.1 GDP Per Capita for Every Countries in Americas

df_9=gapminder%>%filter(continent=="Americas")
df_9=tapply(df_9$gdpPercap,list(df_9$country,df_9$year),mean)
df_9=t(data.frame(df_9))
row.names(df_9)=seq(1952,2007,5)
matplot(seq(1952,2007,5),df_9,type="l",lty=1,xlab="Years",ylab="GDP Per Capita")
legend("topleft",legend=c("Top 1: America","Top 2: Canada"),lty=1,col=3)

(III) Interactive Web Application

• Click me to reach the App

library(shiny)
UI=fluidPage(
  titlePanel("World Facts"),
  sidebarLayout(
    sidebarPanel(
      selectInput(inputId="select",
                  label="Choose a country",
                  choices=unique(gapminder$country)
      ),
      selectInput(inputId="object",
                  label="Choose from the following",
                  choices=c("Life Expectancy","Population","GDP per Capita")
      )
    ),
    mainPanel(plotOutput(outputId="figure"),
              tableOutput(outputId="data")
    )
  )
)

SERVER=function(input,output){
  f=function(temp){
    result=subset(gapminder,gapminder$country==temp)
    return(result)
  }
  output$figure=renderPlot({
    country.name=reactive(input$select)
    dat=f(country.name())
    if (input$object=="Life Expectancy"){
      plot(dat$lifeExp~dat$year,xlim=c(1950,2010),xlab="Year",ylab="Life Expectancy",lty=2,type="l",main=c("Life Expectancy of ",country.name()))
      points(dat$lifeExp~dat$year,pch=19,col=1)
    }
    if (input$object=="Population"){
      plot(dat$pop~dat$year,xlim=c(1950,2010),xlab="Year",ylab="Population",lty=2,type="l",main=c("Population of ",country.name()))
      points(dat$pop~dat$year,pch=19,col=1)
    }
    if (input$object=="GDP per Capita"){
      plot(dat$gdpPercap~dat$year,xlim=c(1950,2010),xlab="Year",ylab="GDP per Capita",lty=2,type="l",main=c("GDP per Capita of ",country.name()))
      points(dat$gdpPercap~dat$year,pch=19,col=1)
    }
  })
  output$data=renderTable(colnames=T,{
    country.name=reactive(input$select)
    dat=f(country.name())
    if (input$object=="Life Expectancy"){
      temp=c()
      temp$year=dat$year
      temp$`Life Expectancy`=dat$lifeExp
      return(temp)
    }
    if (input$object=="Population"){
      temp=c()
      temp$year=dat$year
      temp$pop=dat$pop
      return(temp)
    }
    if (input$object=="GDP per Capita"){
      temp=c()
      temp$year=dat$year
      temp$`GDP per Capita`=dat$gdpPercap
      return(temp)
    }
  })
}

shinyApp(ui=UI,server=SERVER)